Joint pricing-network design and stochastic traffic engineering to manage demand uncertainty

Abstract

I will describe two networking models, together with their optimization techniques, that span several time scales. In the longest time scale, where the goal is capacity planning, I will describe the work of Bienstock, Raskina, Saniee and Wang that considers joint pricing and network design of optical transport networks. Technological innovations are yielding sharply decreasing unit costs. There is also empirical evidence that suggests that the elasticity of bandwidth demand to price is high. Integrating these features in a unified profit-maximizing model leads to a large- scale nonlinear optimization problem. In this work, efficient solution techniques are developed to maximize the carrier's net present value with respect to pricing strategies and investment decisions for technology acquisitions. In the work of Mitra and Wang the time scale is shorter, the network infrastructure is fixed, and a model for stochastic traffic engineering is given in which the optimization is with respect to bandwidth provisioning and route selection. Traffic demands are uncertain, and the objective is to maximize a risk-adjusted measure of network revenue that is generated by serving demands. Considerable attention is given to the appropriate measure of risk in the network model. Risk-mitigation strategies are also advanced. The optimization model, which is based on mean-risk analysis, enables a service provider to maximize a combined measure of mean revenue and revenue risk. The conditions under which the optimization problem is an instance of convex programming are obtained. The solution is shown to satisfy the stochastic efficiency criterion asymptotically. The efficient frontier, which is the set of Pareto optimal pairs of mean revenue and revenue risk, is obtained.